Class 5 - plotting using ggplot2; programming; overview of statistics

Before you start…

Make sure you have the following set and ready:

• Folder you want touse as your working directory exists on your HDD
• Set this folder as working directory (using a menu or setwd("YOUR_PATH))
• Relevant data (if provided) is in your working directory

In the below instructions…

EXERCISE X: are bits of code to execute/practice pieces to do, often with only hints on how to perform them.

Output indicates the typical output you should expect from a given instruction.

Data

We will work on a simple dataset on cholesterol levels from patients. The data presents cholesterol concentrations in plasma in patients before diet, and after 4 & 8 weeks of diet containing one of two types of margarine. The age group of patients is also indicated. Data can be downloaded here: https://raw.githubusercontent.com/wbabik/Practical_computing/teaching/Class_10/data/Cholesterol_Age_R.csv

Code

data_chol <- read.table("https://raw.githubusercontent.com/wbabik/Practical_computing/teaching/Class_10/data/Cholesterol_Age_R.csv",
    sep = ";", header = T,
    stringsAsFactors = T
)

head(data_chol)

  ID Before After4weeks After8weeks Margarine AgeGroup
1  1   6.42        5.83        5.75         B    Young
2  2   6.76        6.20        6.13         A    Young
3  3   6.56        5.83        5.71         B    Young
4  4   4.80        4.27        4.15         A    Young
5  5   8.43        7.71        7.67         B    Young
6  6   7.49        7.12        7.05         A   Middle

Code

summary(data_chol)

       ID            Before       After4weeks     After8weeks    Margarine
 Min.   : 1.00   Min.   :3.910   Min.   :3.700   Min.   :3.660   A:9      
 1st Qu.: 5.25   1st Qu.:5.740   1st Qu.:5.175   1st Qu.:5.210   B:9      
 Median : 9.50   Median :6.500   Median :5.830   Median :5.730            
 Mean   : 9.50   Mean   :6.408   Mean   :5.842   Mean   :5.779            
 3rd Qu.:13.75   3rd Qu.:7.218   3rd Qu.:6.730   3rd Qu.:6.688            
 Max.   :18.00   Max.   :8.430   Max.   :7.710   Max.   :7.670            
   AgeGroup
 Middle:6  
 Old   :7  
 Young :5  
           
           
           

1 ggplot2

1.1 Scatterplot

Code

library(ggplot2)

EXERCISE 1: Make a scatterplot similar to one of the previous excersises, mapping the concentrations of cholesterol on the x and y axes. Use blue squares as points. You may want to use the cex option to increase the default symbol size (cex defines a multiplicative coefficient, that increases or decreases plot elements given number of times).

Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3)
plot1

EXERCISE 2: Let’s improve the plot by removing the annoying gray background. add the theme_...() call to the plot (you can review different predefined versions of it here https://ggplot2.tidyverse.org/reference/ggtheme.html) to produce a cleaner graph. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) + theme_bw()
plot1

EXERCISE 3: An even more aesthetically-pleasing plot can be produced using the “classic” theme. Try also, by addind the theme() definition to the plot, to modify the text element using the following formatting: element_text(size = 20) - which should increase the default font sizing). Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) +
  theme_classic() + theme(text = element_text(size = 20))

### LUB

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) +
  theme_classic(base_size = 20)

### rekomendowana taktyka to modyfikacja warstwy theme()

plot1

EXERCISE 4: Add the geom_smooth aesthetic to the plot, selecting the lm method as its option. Do you know what does lm indicate? Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) + theme_classic() + geom_smooth(method = 'lm') +
  theme(text = element_text(size = 20))
plot1

EXERCISE 5: We can modify the above call to change the appearance of the regression line. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) + theme_classic() + geom_smooth() +
  theme(text = element_text(size = 20))
plot1

1.2 Annotating the plot, other dimensions of data

EXERCISE 6: Try to map the age groups (AgeGroup) to the graph points’ colours. Using the alpha option (taking values 0 - 1 - which indicates the transparency of the regression error band) I decreased the cluttered appearance of the plot, making the error bands a bit more subtle. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks, color = AgeGroup)) +
  geom_point(shape = 15, cex = 3) + theme_classic() + geom_smooth(method = 'lm', alpha = 0.25) +
  theme(text = element_text(size = 20))
plot1

EXERCISE 7: let’s add the labs() layer with a bit more readable axes names. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks, color = AgeGroup)) +
  geom_point(shape = 15, cex = 3) + theme_classic() + geom_smooth(method = 'lm', alpha = 0.25) +
  labs(x = "Concentration before experiment", y = "Concentration after 8 weeks", color = "Age") +
  theme(text = element_text(size = 20))
plot1

1.3 ggplot2 histogram

EXERCISE 8: Using the geom_hist() geometry create a histogram of the After8weeks variable. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = After8weeks)) +
  geom_histogram(fill = 'white', color = 'black', bins = 10) +
  labs(x = "Concentration after 8 weeks") + theme_classic() +
  theme(text = element_text(size = 20))
plot1

EXERCISE 9: Change the histogram so that it displays relative frequencies of data in each bin, and not absolute counts. Inspiration on how to do this can be found here: https://homepage.divms.uiowa.edu/~luke/classes/STAT4580/histdens.html - there are at least two ways of achieving this goal!

Output

Code

### this is one of the methods (inside the geom_hist())
plot1 <- ggplot(data = data_chol, mapping = aes(x = After8weeks)) +
  geom_histogram(fill = 'white', color = 'black', bins = 10, aes(y = ..count../sum(..count..))) +
  labs(x = "Concentration after 8 weeks") +theme_classic() +
  theme(text = element_text(size = 20))
plot1

1.4 Boxplot

EXERCISE 10: geom_boxplot() can be used to visualise categorical data. In base R this is achieved by using the boxplot() function:

Code

boxplot(Before ~ AgeGroup, data = data_chol,
        xlab = "Age group", ylab = "Cholesterol conc. before the experiment")

Try to produce such plot, showing the cholesterol concentrations before the diet, categorised by age groups, in ggplot2. Use ?geom_boxplot and if needed the book https://ggplot2-book.org/collective-geoms.html to find out how to achieve this. On such a boxplot - what is the meaning of: the boundaries of each box, the ends of the whiskers and the additional points added to the plot?

Output

Code

plot2 <- ggplot(data = data_chol, mapping = aes(x = AgeGroup, y = Before)) +
  geom_boxplot() +
  labs(x = "Age Group", y = "Cholesterol before experiment") +
  theme_classic() + theme(text = element_text(size = 25))
plot2

1.5 Customizing plot elements - scales, axes, legends

In our previous example we used in all cases default colours supplied by ggplot. Let’s explore the ways we can modify legends. First - produce the below plot based on the `Diet_R.csv’ data, it shows the relationship between body height and post-diet weight in three different diet groups. You can load the data here:

Code

mydata <- na.omit(read.table("https://raw.githubusercontent.com/wbabik/Practical_computing/teaching/Class_10/data/Diet_R.csv", sep = ",", header = T))

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight")
myplot

EXERCISE 11: Let’s try to build a box-plot using this dataset, the gender should be used linked to a colour of boxes on the plot, the diet types shoud appear on the x-axis, and the weight after 6 weeks should be the response variable.

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = as.factor(Diet),
                                            color = as.factor(gender),
                                            y = weight6weeks,
                                            fill = as.factor(gender))) +
  geom_boxplot(alpha = 0.3, outlier.shape = NA) + 
  labs(x = "Diet type", y = "Weight after diet", colour = "Gender", fill = "Gender") +
  theme_classic()
myplot

To make the plot a bit more comprehensive, we can modify scales linked to colour, fill and, for example, x axis. Use scale_colour_viridis_d() (a discrete version of the viridis palette), scale_fill_viridis_d and scale_x_discrete to modify the above plot and reproduce the below example. Note: if you don;t have the viridis library installed, execute first install.packages('viridis').

Code

myplot <- ggplot(data = mydata, mapping = aes(x = as.factor(Diet),
                                            color = as.factor(gender),
                                            y = weight6weeks,
                                            fill = as.factor(gender))) +
  geom_boxplot(alpha = 0.3, outlier.shape = NA) + 
  labs(x = "Diet type", y = "Weight after diet", colour = "Gender", fill = "Gender") +
  scale_colour_viridis_d(labels = c('female', 'male')) +
  scale_fill_viridis_d(labels = c('female', 'male')) +
  scale_x_discrete(labels = c('vegan', 'lacto-ovo', 'vegeterian')) +
  theme_classic() +
  theme(text = element_text(size = 20))
myplot

2 Elements of programming in R

Code execution and flow control

In R, you can use very similar programming constructions as in other programming languages. For example, to execute certain parts of your code conditionally, you can use the if ... else ... syntax:

if (condition) {
  # code to execute if condition is TRUE
} else {
  # code to execute if condition is FALSE
}

On the other hand, you can use the for loop to iterate over a sequence of elements:

for (i in 1:10) {
  print(i)
}

The below code shows you a simple example of binding these two command together. In it, we print even-numbered elements of the built-in letter vector LETTERS for letters from 4th until 17th, using the conditional and loop statements:

Code

for (i in 4:17) {
  if (i %% 2 == 0) {
    print(LETTERS[i])
  }
}

[1] "D"
[1] "F"
[1] "H"
[1] "J"
[1] "L"
[1] "N"
[1] "P"

In this excersie you will take advantage of these two ways of controlling the flow of your code. We will use an old method to estimate the value of \(\pi\) - the Monte Carlo method. The idea is to randomly select points in a square with dimensions 1x1, and then check how many of them fall into a circle with radius 0.5 (or diameter 1). The ratio of points in the circle to all points in the square should be close to \(\pi/4\). The reason for this is that the area of the circle is \(\pi r^2\), and the area of the square is \((2r)^2 = 4r^2\). The ratio of the areas is \(\pi/4\).

EXERCISE 12: Write a piece of code the executes this method. Use the runif() function to generate random numbers between 0 and 1. Execute the method for 500, 1000 and 10000 iterations - does it improve the estimate of the estimated value of \(\pi\)? Remember, that to checking if points fall into a circle of a given radius, you can use the Pythagorean theorem: \(x^2 + y^2 < r^2\).

Output

Code

N <- 1000
inside <- 0
all <- 0

for (i in 1:N) {
  x <- runif(1)
  y <- runif(1)
  if (x^2 + y^2 < 1) {
    inside <- inside + 1
  }
  all <- all + 1
}

pi_est <- 4 * inside / all
pi_est

[1] 3.168

If you are curious how to perform this method without using the loop and conditional instructions, check out the code below.

Code

n <- 1000
x <- runif(n)
y <- runif(n)
inside <- sum(x^2 + y^2 < 1)
pi_est <- 4 * inside / n
# pi_est

3 Overview of statistics

3.1 Descriptive statistics

EXERCISE 13: Calculate the mean and standard deviation of the After8weeks variable in the data_chol dataset. Use the mean() and sd() functions to do this.

Output

Code

mean(data_chol$After8weeks)

[1] 5.778889

Code

sd(data_chol$After8weeks)

[1] 1.101912

3.2 Hypothesis testing

EXERCISE 14: Perform a t-test to check if the mean cholesterol concentration after 8 weeks of diet is different between the two types of margarine. Use the t.test() function to do this. Note: you can - as always - specify the dependent and independent variables using the formula y ~ x notation.

Output

Code

t.test(After8weeks ~ Margarine, data = data_chol)

    Welch Two Sample t-test

data:  After8weeks by Margarine
t = -1.1253, df = 13.508, p-value = 0.2801
alternative hypothesis: true difference in means between group A and group B is not equal to 0
95 percent confidence interval:
 -1.6892688  0.5292688
sample estimates:
mean in group A mean in group B 
       5.488889        6.068889 

3.3 Regression

EXERCISE 15: Perform a linear regression to check if the cholesterol concentration after 8 weeks of diet can be predicted by the concentration before the diet. Use the lm() function to do this.

Output

Code

model <- lm(After8weeks ~ Before, data = data_chol)
summary(model)

Call:
lm(formula = After8weeks ~ Before, data = data_chol)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.20843 -0.15401  0.01823  0.12825  0.27904 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.09513    0.20283  -0.469    0.645    
Before       0.91670    0.03115  29.428 2.32e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.153 on 16 degrees of freedom
Multiple R-squared:  0.9819,    Adjusted R-squared:  0.9807 
F-statistic:   866 on 1 and 16 DF,  p-value: 2.321e-15

4 Appendix - more advanced ggplot2

EXERCISE A: An alternative way of coding age gropups - instead of mapping it to colours - may be splitting the groups by so called facets, which presents subsets of data on separate subplots using a common scale. To achieve this you should use the facet_wrap() function, which takes a formula of the form ~ A, where A indicates a variable from the dataset that defines the split of the graph area into subplots. (An analogous function facet_grid() handles well two-sided formulas A ~ B that define a grid of plots). Try to recreate the below plot - it may look better with the theme_bw() style, instead of the “classic’ one. Output

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks, color = AgeGroup)) +
  facet_wrap( ~ AgeGroup) +
  geom_point(shape = 15, cex = 3) + theme_bw() + geom_smooth(method = 'lm') +
  labs(x = "Concentration before experiment", y = "Concentration after 8 weeks", color = "Age") +
  theme(text = element_text(size = 20))
plot1

EXERCISE B: Modify the histogram to add a kernel density estimator to it (it is an analogue of the density() function we have used earlier).

Output

Code

### This is the second way of turning counts to frequencies in a ggplot2 histogram
plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = ..density..)) +
  geom_histogram(fill = 'white', color = 'black', bins = 10) +
  geom_density(color = 'red', fill = 'red', alpha = 0.15) +
  labs(x = "Concentration after 8 weeks") + theme_classic() +
  theme(text = element_text(size = 20))
plot1

4.1 Grid of plots

If our goal is to place multiple unrelated plots on one figure - we may as well use the ggpubr package and its ggarrange() function.

Code

# install.packages('ggpubr') # if needed
library(ggpubr)

EXERCISE C: First - produce 4 plots and save them to 4 differently named objects. These can be some of the plots we have generated so far:

Code

plot1 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After8weeks)) +
  geom_point(color = 'blue', shape = 15, cex = 3) + theme_classic() +
  theme(text = element_text(size = 12))

plot2 <- ggplot(data = data_chol, mapping = aes(x = Before, y = ..density..)) +
  geom_histogram(fill = 'white', color = 'black', bins = 10) +
  geom_density(color = 'red', fill = 'red', alpha = 0.15) +
  theme_classic() +
  theme(text = element_text(size = 12))

plot3 <- ggplot(data = data_chol, mapping = aes(x = Before, y = After4weeks, color = AgeGroup)) +
  geom_point(shape = 15, cex = 3) + theme_classic() + geom_smooth(method = 'lm', alpha = 0.25) +
  theme(text = element_text(size = 12))

plot4 <- ggplot(data = data_chol, mapping = aes(x = After4weeks, y = After8weeks, color = AgeGroup)) +
  geom_point(shape = 15, cex = 3) + theme_classic() + geom_smooth(method = 'lm', alpha = 0.25) +
  theme(text = element_text(size = 12))

Now - produce a grid plot from the four subplots using the ggarrange() function. Label the subplots as A, B, C, D.

Code

gridplot <- ggarrange(plot1, plot2, plot3, plot4,
                      labels = c('A', 'B', 'C', 'D'), legend = 'bottom', common.legend = T)
gridplot

4.2 Box-plots continued

EXERCISE D: a boxplot may be more informative if we add raw data to it. It can be done in many ways - e.g., to achieve an effect similar to this one: https://bit.ly/31estrN. Try to produce a similar plot using additional data (file Diet_R.csv, which presents weight loss of patients on three different diets). Before using the data clean it from all missing values (na.omit()). Data can be found here: https://raw.githubusercontent.com/wbabik/Practical_computing/teaching/Class_10/data/Diet_R.csv

Code

mydata <- na.omit(read.table("https://raw.githubusercontent.com/wbabik/Practical_computing/teaching/Class_10/data/Diet_R.csv", sep = ",", header = T))

To achieve the below effect remember to include the following components: use geom_boxplot() and geom_point() to map data to visuals; map genders to box colours; for both points and boxes you can set alpha option to something <1 to ensure that they are a bit teansparent and hence less tiring to our eyes.

Output

Code

plot3 <- ggplot(data = mydata, mapping = aes(x = as.factor(Diet),
                                            color = as.factor(gender),
                                            y = weight6weeks,
                                            fill = as.factor(gender))) +
  geom_boxplot(alpha = 0.3, outlier.shape = NA) + 
  theme_classic() +
  geom_point(color = "black",
             position = position_jitterdodge(jitter.width = 0.1),
             size = 3, alpha = 0.5, stroke = 0) +
  theme(text = element_text(size = 25)) +
  labs(x = "Diet type", y = "Weight after 6 weeks",
       color = "Gender", fill = "Gender")

plot3

EXERCISE E: add anothe layer to the plot - using the scale_colour_manual() component change colours used on the plot to purple, organe and dark grey for diets 1, 2 and 3. In order to achieve this you should assign these colours to values of the scale.

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'))
myplot

EXERCISE F: to give some more space for the width of the plot move the legend (by modifying the theme() function call) to the bottom of the plot.

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20), legend.position = "bottom") +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'))
myplot

EXERCISE G: by specifying guide = "none" in the scale layer, you can remove the legend altogether - try it.

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'), guide = "none")
myplot

EXERCISE H: by using the labels option in the scale layer you can easily rename the categories presented in the lgend. Try renaming them to vegan (diet 1), lacto-ovo (diet 2) and vegetarian (diet 3).

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20), legend.position = "bottom") +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'),
                      labels = c('vegan', 'lacto-ovo', 'vegetarian'))
myplot

EXERCISE I: similarly to using the xlim/ylim options in the plot() function, you can modify the plotting area of a ggplot graph. There are two way to achieve this. The first involves adding the xlim() (check ?xlim for details) or ylim() layers (or both). The other works by modifying the coordinate system used and adding the layer: coord_cartesian(xlim = c(...)). Which produces which plot below? How do they differ in handling the plotted data?

Output

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20), legend.position = "bottom") +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'),
                      labels = c('vegan', 'lacto-ovo', 'vegetarian')) +
  xlim(c(160, 175))
myplot

Code

myplot <- ggplot(data = mydata, mapping = aes(x = Height, y = weight6weeks, colour = as.factor(Diet))) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_classic() +
  theme(text = element_text(size = 20), legend.position = "bottom") +
  labs(x = "Height", y = "Weight after 6 weeks", colour = "Diet type", title = "Height vs. weight") +
  scale_colour_manual(values = c('purple', 'orange', 'darkgrey'),
                      labels = c('vegan', 'lacto-ovo', 'vegetarian')) +
  coord_cartesian(xlim = c(160, 175))
myplot

4.3 Barplot

Code

myplot <- ggplot(data = mydata, mapping = aes(x = as.factor(Diet),
                                            fill = as.factor(gender),
                                            y = ..count..)) +
  geom_bar() + 
  labs(x = "Diet type", y = "Count", fill = "Diet") +
  scale_x_discrete(labels = c('vegan','lacto-ovo', 'vegeterian')) +
  scale_fill_discrete(labels = c('f', 'm')) +
  theme_classic() +
  theme(text = element_text(size = 20))

# ggplot2 automatically counts things (..count.. is optional)

myplot

Code

myplot <- ggplot(data = mydata, mapping = aes(x = as.factor(Diet),
                                            fill = as.factor(gender),
                                            y = ..count..)) +
  geom_bar(position = "dodge") + 
  labs(x = "Diet type", y = "Count", fill = "Diet") +
  scale_x_discrete(labels = c('vegan','lacto-ovo', 'vegeterian')) +
  scale_fill_discrete(labels = c('f', 'm')) +
  theme_classic() +
  theme(text = element_text(size = 20))

# ggplot2 automatically counts things (..count.. is optional)

myplot

4.4 Error bars

Code

library(tidyverse)

summ <- tibble(Age = levels(data_chol$AgeGroup),
               mean = by(data_chol$After8weeks, data_chol$AgeGroup, mean, na.rm = T),
               se = by(data_chol$After8weeks, data_chol$AgeGroup,
                       function(x) sd(x, na.rm = T)/sqrt(length(x))))

myplot <- ggplot(data = summ, mapping = aes(x = Age, y = mean,
                                            ymin = mean-se, ymax = mean+se)) +
  geom_bar(stat = 'identity', fill = 'white', colour = 'black') +
  geom_errorbar(width = 0.5) +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(y = "Cholesterol conc. after 8 weeks") +
  scale_x_discrete(limits = c('Young', 'Middle', 'Old'))
myplot

Code

myplot <- ggplot(data = summ, mapping = aes(x = Age, y = mean,
                                            ymin = mean-se, ymax = mean+se)) +
  geom_point(colour = 'black', cex = 5) +
  geom_errorbar(width = 0.5) +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(y = "Cholesterol conc. after 8 weeks") +
  scale_x_discrete(limits = c('Young', 'Middle', 'Old'))
myplot

Code

myplot <- ggplot(data = summ, mapping = aes(x = Age, y = mean,
                                            ymin = mean-se, ymax = mean+se)) +
  geom_point(colour = 'black', cex = 5) +
  geom_errorbar(width = 0.5) +
  theme_classic() +
  theme(text = element_text(size = 20)) +
  labs(y = "Cholesterol conc. after 8 weeks") +
  scale_x_discrete(limits = c('Young', 'Middle', 'Old')) +
  ylim(c(0, 6.5))
myplot

4.5 Saving the plots

Use the ggsave function - it provides some powerful plot saving routines.

EXERCISE J: Save one of the last plots to a JPG file and to a PDF file. Rescale the pdf to about 80% of the original plot size. Check ?ggsave for more information. Compare what happens when you save the plot to a PDF file directly from the plotting console.